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- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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- Craddock, Nick, et al.
(författare)
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Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls
- 2010
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 464:7289, s. 713-720
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Tidskriftsartikel (refereegranskat)abstract
- Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to have an important role in genetic susceptibility to common disease. To address this we undertook a large, direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed,19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated similar to 50% of all common CNVs larger than 500 base pairs. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease-IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis and type 1 diabetes, and TSPAN8 for type 2 diabetes-although in each case the locus had previously been identified in single nucleotide polymorphism (SNP)-based studies, reflecting our observation that most common CNVs that are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs that can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases.
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| 7. |
- Romagnoni, A, et al.
(författare)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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| 8. |
- Niemi, MEK, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 9. |
- Cho, Jung, et al.
(författare)
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EMM-25 : The Structure of Two-Dimensional 11 x 10 Medium-Pore Borosilicate Zeolite Unraveled Using 3D Electron Diffraction
- 2021
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Ingår i: Chemistry of Materials. - : American Chemical Society (ACS). - 0897-4756 .- 1520-5002. ; 33:11, s. 4146-4153
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Tidskriftsartikel (refereegranskat)abstract
- The structure of the novel medium-pore borosilicate zeolite EMM-25 has been determined by continuous rotation electron diffraction (cRED). EMM-25 crystallizes in the space group Cmcm with unit cell parameters a = 11.055, b = 22.912, and c = 24.914 angstrom and a composition of IC4H8(C11H25N)(2)I (2)[Si112.5B3.5O232]. The EMM-25 framework possesses a two-dimensional channel system composed of 10-ring channels connected via 11-ring windows. Its channel system is analogous to that of the medium-pore zeolite NU-87 framework but with 11- rather than 12-ring windows, suggesting a different shape selectivity. EMM-25 was first obtained using 1,4-bis(N-methyl-N,N-dihexylammonium)butane as an organic structure directing agent (OSDA). Based on a molecular docking study of the OSDA within the pores of the determined framework structure, a new ammonium dication OSDA with an improved fit was devised. By using this new OSDA, the synthesis time was reduced 80%, from 52 to just 10 days. Furthermore, cRED data revealed a structural disorder of the EMM-25 framework present as swinging zigzag chains. The introduction of the disorder, which is a consequence of geometry relaxation, was crucial for an accurate structure refinement. Lastly, the cRED data from as-made EMM-25 showed residual potential consistent with the location of the OSDA position determined from the Rietveld refinement, concluding a complete refinement of the as-made structure based on the cRED data.
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